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Effective real-time vehicle tracking using discriminative sparse coding on local patches

[+] Author Affiliations
XiangJun Chen

Nanjing University, School of Electronic Science and Engineering, Xianlin Road, No. 163, Qixia District, Nanjing 210046, China

Jiangsu University of Technology, Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City, Zhongwu Road, No. 1801, Zhonglou District, Changzhou 213001, China

Feiyue Ye

Jiangsu University of Technology, Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City, Zhongwu Road, No. 1801, Zhonglou District, Changzhou 213001, China

Yaduan Ruan, Qimei Chen

Nanjing University, School of Electronic Science and Engineering, Xianlin Road, No. 163, Qixia District, Nanjing 210046, China

J. Electron. Imaging. 25(1), 013035 (Feb 23, 2016). doi:10.1117/1.JEI.25.1.013035
History: Received October 8, 2015; Accepted January 27, 2016
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Abstract.  A visual tracking framework that provides an object detector and tracker, which focuses on effective and efficient visual tracking in surveillance of real-world intelligent transport system applications, is proposed. The framework casts the tracking task as problems of object detection, feature representation, and classification, which is different from appearance model-matching approaches. Through a feature representation of discriminative sparse coding on local patches called DSCLP, which trains a dictionary on local clustered patches sampled from both positive and negative datasets, the discriminative power and robustness has been improved remarkably, which makes our method more robust to a complex realistic setting with all kinds of degraded image quality. Moreover, by catching objects through one-time background subtraction, along with offline dictionary training, computation time is dramatically reduced, which enables our framework to achieve real-time tracking performance even in a high-definition sequence with heavy traffic. Experiment results show that our work outperforms some state-of-the-art methods in terms of speed, accuracy, and robustness and exhibits increased robustness in a complex real-world scenario with degraded image quality caused by vehicle occlusion, image blur of rain or fog, and change in viewpoint or scale.

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Citation

XiangJun Chen ; Feiyue Ye ; Yaduan Ruan and Qimei Chen
"Effective real-time vehicle tracking using discriminative sparse coding on local patches", J. Electron. Imaging. 25(1), 013035 (Feb 23, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.1.013035


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